class_docstr: Decision List Classifier
  
  Currently a slight variant of SkopeRules from skope-rules.
  https://github.com/scikit-learn-contrib/skope-rules
docstr: Predicts on provided instances.
  
  Args:
      X: Numpy array for instances.
  
  Returns:
      Predicted class label per instance.
docstr_tok: ["Predicts","on","provided","instances",".","Args",":","X",":","Numpy","array","for","instances",".","Returns",":","Predicted","class","label","per","instance","."]
code: def predict(self, X):
      """Predicts on provided instances.
  
      Args:
          X: Numpy array for instances.
  
      Returns:
          Predicted class label per instance.
      """
  
      # let predict_proba clean X and check if we are fitted
      scores = self.predict_proba(X)
      return self.classes_[np.argmax(scores, axis=1)]
  
imports: from itertools import count
  from sklearn.base import ClassifierMixin
  from sklearn.utils.validation import check_is_fitted
  from ..api.base import ExplainerMixin, ExplanationMixin
  from ..utils._explanation import gen_name_from_class, gen_global_selector, gen_local_selector, gen_perf_dicts
  from copy import deepcopy
  import numpy
  import pandas
  import re
  from ..utils._clean_x import preclean_X
  from ..utils._clean_simple import clean_dimensions, typify_classification
  from ..utils._unify_data import unify_data
  import logging
label_desc: Use image node_id 3 for calling the DecisionListClassifier obj's underlying member method code with example usage: obj.predict(X) and returns: self